Automated Recognition of Bioacoustic Signals: a Review of Methods and Applications
Main Article Content
Keywords
automated enviromental monitoring, bioacoustics, acoustic signal processing, pattern recognition.
Abstract
During the past decade, numerous research studies and applications on automated bioacoustic monitoring have been published; however, such studies are scattered in the literature of engineering and life sciences. This paper presents a review on fundamental concepts of automated acoustic monitoring. Our aim is to compare and categorize —in a taxonomy of techniques DSP/PR— the contributions of published research studies and applications; in order to suggest some directions for future research and highlight challenges and opportunities related to the deployment of this technology in Colombia.
PACS: 43.60.-c, 43.60.Lq, 43.80.-n
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References
[1] M. Depraetere, S. Pavoine, F. Jiguet, A. Gasc, S. Duvail, and J. Sueur, “Monitoring animal diversity using acoustic indices: Implementation in a temperate woodland,” Ecological Indicators, vol. 13, no. 1, pp. 46 – 54, 2012.
[2] D. M. Marsh and P. C. Trenham, “Current trends in plant and animal population monitoring,” Conservation Biology, vol. 22, no. 3, pp. 647–655, 2008.
[3] M. A. Acevedo, C. J. Corrada-Bravo, H. Corrada-Bravo, L. J. Villanueva-Rivera, and T. M. Aide, “Automated classification of bird and amphibian calls using machine learning: A comparison of methods,” Ecological Informatics, vol. 4, no. 4, pp. 206 – 214, 2009.
[4] C.-H. Lee, C.-C. Han, and C.-C. Chuang, “Automatic classification of bird species from their sounds using two-dimensional cepstral coefficients,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 16, no. 8, pp. 1541 –1550, Nov. 2008.
[5] J. E. Botero, D. Arbeláez, and G. M. Lentijo, “Métodos para estudiar las aves,” Biocarta, no. 8, pp. 1–4, Jul. 2005. [Online]. Available: http://www.rnoa.org/sco/ pdf/Biocarta008.pdf
[6] W. J. Sutherland, Ecological census techniques: a handbook, 2nd ed. Cambridge University Press, 2006.
[7] C. Ten Cate, “Birdsong and Evolution,” in Nature’s music: the science of birdsong, 2004, ch. 10, pp. 296–317.
[8] W. Chu and D. T. Blumstein, “Noise robust bird song detection using syllable pattern-based hidden Markov models,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011, may 2011, pp. 345 –348.
[9] B. R. Grant and P. R. Grant, “Hybridization and speciation in darwin’s finches: the role of sexual imprinting on a culturally transmitted trait,” Endless Forms: Species and Speciation, pp. 404–422, 1998.
[10] R. Bardeli, D. Wolff, F. Kurth, M. Koch, K. H. Tauchert, and K.-H. Frommolt, “Detecting bird sounds in a complex acoustic environment and application to bioacoustic monitoring,” Pattern Recognition Letters, vol. 31, no. 12, pp. 1524–1534, Sep. 2010.
[11] D. C. Cugler, C. B. Medeiros, and L. F. Toledo, “Managing animal sounds-some challenges and research directions,” in Proceedings V eScience Workshop-XXXI Brazilian Computer Society Conference, 2011.
[12] A. Farnsworth and R. W. Russell, “Monitoring flight calls of migrating birds from an oil platform in the northern gulf of mexico,” Journal of Field Ornithology, vol. 78, no. 3, pp. 279–289, 2007.
[13] D. Mennill and S. Vehrencamp, “Context-dependent functions of avian duets revealed through microphone array recordings and multi-speaker playback,” Current Biology, vol. 18, pp. 1314–1319, 2008.
[14] K. J. Odom and D. J. Mennill, “A quantitative description of the vocalizations and vocal activity of the barred owl,” The Condor, vol. 112, no. 3, pp. 549–560, 2010.
[15] S. Gaunt and A. McCallum, “Birdsong and Conservation,” in Nature’s music: the science of birdsong, 2004, ch. 12, pp. 343–362.
[16] A. Kirschel, M. Cody, Z. Harlow, V. Promponas, E. Vallejo, and C. Taylor, “Territorial dynamics of mexican ant-thrushes formicarius moniliger revealed by individual recognition of their songs,” Ibis, vol. 153, no. 2, pp. 255–268, 2011.
[17] D. T. Blumstein, D. J. Mennill, P. Clemins, L. Girod, K. Yao, G. Patricelli, J. L. Deppe, A. H. Krakauer, C. Clark, K. A. Cortopassi, S. F. Hanser, B. McCowan, A. M. Ali, and A. N. G. Kirschel, “Acoustic monitoring in terrestrial environments using microphone arrays: applications, technological considerations and prospectus,” Journal of Applied Ecology, vol. 48, no. 3, pp. 758–767, 2011.
[18] D. Chesmore, “The automated identification of taxa: Concepts and applications,” in Automated Taxon Identification in Systematics: Theory, Approaches and Applications, ser. Systematics Association special volumes, N. MacLeod, Ed. Boca Raton, FL: CRC Press, 2008, vol. 74, ch. 6, pp. 83–100.
[19] D. Chesmore, K.-H. Frommolt, D. Wolff, R. Bardeli, and S. Huebner, “Computational bioacoustics: New tools for assessing biological diversity,” May 2008, side Event at the ninth meeting of the Conference of the Parties (COP 9). Bonn, Germany.
[20] M. A. Acevedo and L. J. Villanueva-Rivera, “Using automated digital recording systems as effective tools for the monitoring of birds and amphibians,” Wildlife Society Bulletin, vol. 34, pp. 211 – 214, 2006.
[21] D. W. Armitage and H. K. Ober, “A comparison of supervised learning techniques in the classification of bat echolocation calls,” Ecological Informatics, vol. 5, no. 6, pp. 465 – 473, 2010.
[22] F. Briggs, X. Fern, and R. Raich, “Acoustic classification of bird species from syllables: an empirical study,” Oregon State University, Tech. Rep., 2009.
[23] E. P. Kasten, P. K. McKinley, and S. H. Gage, “Ensemble extraction for classification and detection of bird species,” Ecological Informatics, vol. 5, no. 3, pp. 153 –166, 2010.
[24] T. S. Brandes, “Automated sound recording and analysis techniques for bird surveys and conservation,” Bird Conservation International, vol. 18, no. S1, pp. S163–S173, 2008.
[25] R. Mason, P. Roe, M. Towsey, J. Zhang, J. Gibson, and S. Gage, “Towards an acoustic environmental observatory,” in eScience, 2008. eScience ’08. IEEE Fourth International Conference on, 2008, pp. 135–142.
[26] R. S. Rempel, C. M. Francis, J. N. Robinson, and M. Campbell, “Comparison of audio recording system performance for detecting and monitoring songbirds,” Journal of Field Ornithology, vol. 84, no. 1, pp. 86–97, 2013.
[27] D. Mennill, M. Battiston, D. R. Wilson, J. R. Foote, and S. M. Doucet, “Field test of an affordable, portable, wireless microphone array for spatial monitoring of animal ecology and behaviour,” Methods in Ecology and Evolution, vol. 3, pp. 704–712, 2012.
[28] K. A. Hobson, R. S. Rempel, H. Greenwood, B. Turnbull, and S. L. Van Wilgenburg, “Acoustic surveys of birds using electronic recordings: new potential from an omnidirectional microphone system,” Wildlife Society Bulletin, pp. 709–720, 2002.
[29] L. A. Venier, S. B. Holmes, G. W. Holborn, K. A. Mcilwrick, and G. Brown, “Evaluation of an automated recording device for monitoring forest birds,” Wildlife Society Bulletin, vol. 36, no. 1, pp. 30–39, 2012.
[30] T. S. Brandes, “Feature vector selection and use with hidden Markov models to identify frequency-modulated bioacoustic signals amidst noise,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 16, no. 6, pp. 1173 –1180, aug 2008.
[31] P. Caycedo-Rosales, “Estudio comparativo del canto entre poblaciones del soterrey de apolinar (cistothorus apolinari, troglodytidae) presentes en la cordillera oriental de los andes colombianos,” Universidad Nacional de Colombia, 2000, tesis de pregrado.
[32] B. Lakshminarayanan, R. Raich, and X. Fern, “A syllable-level probabilistic framework for bird species identification,” in Proceedings of the Fourth International Conference on Machine Learning and Applications. Los Alamitos, CA, USA: IEEE Computer Society, Dec. 2009, pp. 53–59.
[33] R. Bardeli, “Similarity search in animal sound databases,” IEEE Transactions on Multimedia, vol. 11, no. 1, pp. 68 –76, jan 2009.
[34] F. Briggs, R. Raich, and X. Z. Fern, “Audio classification of bird species: A statistical manifold approach,” in Proceedings of the Ninth IEEE International Conference on Data Mining. Los Alamitos, CA, USA: IEEE Computer Society, Dec. 2009, pp. 51–60.
[35] C.-J. Huang, Y.-J. Yang, D.-X. Yang, and Y.-J. Chen, “Frog classification using machine learning techniques,” Expert Systems with Applications, vol. 36, no. 2, pp. 3737–3743, 2009.
[36] D. Todt, “From birdsong to speech: a plea for comparative approaches,” Anais da Academia Brasileira de Ciências, vol. 76, no. 2, pp. 201–208, 2004.
[37] L. Ranjard and H. A. Ross, “Unsupervised bird song syllable classification using evolving neural networks,” Journal of the Acoustical Society of America, vol. 123, no. 6, pp. 4358 – 4368, 2008.
[38] M. Bramer, Principles of Data Mining. Springer, 2007.
[39] S. Fagerlund, “Bird species recognition using support vector machines,” EURASIP Journal on Advances in Signal Processing, vol. 2007, no. 1, pp. 64–64, 2007.
[40] I. Urazghildiiev, C. Clark, T. Krein, and S. Parks, “Detection and Recognition of North Atlantic Right Whale Contact Calls in the Presence of Ambient Noise,” IEEE Journal of Oceanic Engineering, vol. 34, no. 3, pp. 358–368, Jul. 2009.
[41] F. Briggs, B. Lakshminarayanan, L. Neal, X. Z. Fern, R. Raich, S. J. K. Hadley, A. S. Hadley, and M. G. Betts, “Acoustic classification of multiple simultaneous bird species: A multi-instance multi-label approach,” Journal of the Acoustical Society of America, vol. 131, no. 6, pp. 4640–4650, Oct. 2012.
[42] C.-H. Chou, P.-H. Liu, and B. Cai, “On the studies of syllable segmentation and improving MFCCs for automatic birdsong recognition,” in Proceedings of the 2008 IEEE Asia-Pacific Services Computing Conference. Washington, DC, USA: IEEE Computer Society, 2008, pp. 745–750.
[43] A. Härmä, “Automatic identification of bird species based on sinusoidal modeling of syllables,” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP ’03, vol. 5, Apr. 2003, pp. 545–548.
[44] D. Stowell and M. D. Plumbley, “Birdsong and C4DM: A survey of UK birdsong and machine recognition for music researchers,” Centre for Digital Music, Queen Mary, University of London, London, UK, Tech. Rep. C4DM-TR-09-12, Jul. 2011.
[45] L. Neal, F. Briggs, R. Raich, and X. Z. Fern, “Time-frequency segmentation of bird song in noisy acoustic environments,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011, May 2011, pp. 2012 –2015.
[46] C.-H. Lee, S.-B. Hsu, J.-L. Shih, and C.-H. Chou, “Continuous Birdsong Recognition Using Gaussian Mixture Modeling of Image Shape Features,” IEEE Transactions on Multimedia, vol. 15, pp. 454–464, 2013.
[47] D. Mitrovic, M. Zeppelzauer, and C. Breiteneder, “Features for content-based audio retrieval,” in Advances in Computers: Improving the Web, ser. Advances in Computers, M. V. Zelkowitz, Ed. Elsevier, 2010, vol. 78, pp. 71 – 150.
[48] S. Ntalampiras, I. Potamitis, and N. Fakotakis, “Probabilistic Novelty Detection for Acoustic Surveillance Under Real-World Conditions,” IEEE Transactions on Multimedia, vol. 13, no. 4, pp. 713–719, Aug. 2011.
[49] A. Selin, J. Turunen, and J. T. Tanttu, “Wavelets in recognition of bird sounds,” EURASIP J. Appl. Signal Process., vol. 2007, no. 1, pp. 141–141, Jan. 2007.
[50] J. L. Goyette, R. W. Howe, A. T. Wolf, and W. D. Robinson, “Detecting tropical nocturnal birds using automated audio recordings,” Journal of Field Ornithology, vol. 82, no. 3, pp. 279–287, 2011.
[51] C. M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics), 1st ed. Springer, 2007.
[52] R. Duda, P. Hart, and D. Stork, Pattern Classification. Wiley, 2001.
[53] L. R. Rabiner, “A tutorial on hidden Markov models and selected applications in speech recognition,” Proceedings of the IEEE, vol. 77, no. 2, pp. 257 –286, May 1989.
[54] D. Chesmore, “Automated bioacoustic identification of species,” Anais da Academia Brasileira de Ciências, vol. 76, no. 2, pp. 436–440, 2004.
[55] J. Wimmer, M. Towsey, B. Planitz, P. Roe, and I. Williamson, “Scaling acoustic data analysis through collaboration and automation,” in e-Science (e-Science), 2010 IEEE Sixth International Conference on. IEEE, 2010, pp. 308–315.
[56] W. J. Sutherland, From individual behaviour to population ecology. Oxford University Press Inc., 1996.
[57] P. K. McGregor, T. M. Peake, and G. Gilbert, “Communication behaviour and conservation,” in Behaviour and Conservation. Cambridge University Press, 2000.
[58] H. Slabbekoorn, “Singing in the wild: the ecology of birdsong,” in Nature’s music: the science of birdsong, 2004, ch. 1, pp. 178–2005.
[59] V. Deecke, J. Ford, and P. Spong, “Quantifying complex patterns of bioacoustic variation: use of a neural network to compare killer whale (orcinus orca) dialects,” The Journal of the Acoustical Society of America, vol. 105, p. 2499, 1999.
[60] H. Mills, “Automatic detection and classification of nocturnal migrant bird calls,” The Journal of the Acoustical Society of America, vol. 97, p. 3370, 1995.
[61] A. Taylor, “Bird flight call discrimination using machine learning,” The Journal of the Acoustical Society of America, vol. 97, p. 3370, 1995.
[62] W. M. Tyler, “The call-notes of some nocturnal migrating birds,” The Auk, pp. 132–141, 1916.
[2] D. M. Marsh and P. C. Trenham, “Current trends in plant and animal population monitoring,” Conservation Biology, vol. 22, no. 3, pp. 647–655, 2008.
[3] M. A. Acevedo, C. J. Corrada-Bravo, H. Corrada-Bravo, L. J. Villanueva-Rivera, and T. M. Aide, “Automated classification of bird and amphibian calls using machine learning: A comparison of methods,” Ecological Informatics, vol. 4, no. 4, pp. 206 – 214, 2009.
[4] C.-H. Lee, C.-C. Han, and C.-C. Chuang, “Automatic classification of bird species from their sounds using two-dimensional cepstral coefficients,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 16, no. 8, pp. 1541 –1550, Nov. 2008.
[5] J. E. Botero, D. Arbeláez, and G. M. Lentijo, “Métodos para estudiar las aves,” Biocarta, no. 8, pp. 1–4, Jul. 2005. [Online]. Available: http://www.rnoa.org/sco/ pdf/Biocarta008.pdf
[6] W. J. Sutherland, Ecological census techniques: a handbook, 2nd ed. Cambridge University Press, 2006.
[7] C. Ten Cate, “Birdsong and Evolution,” in Nature’s music: the science of birdsong, 2004, ch. 10, pp. 296–317.
[8] W. Chu and D. T. Blumstein, “Noise robust bird song detection using syllable pattern-based hidden Markov models,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011, may 2011, pp. 345 –348.
[9] B. R. Grant and P. R. Grant, “Hybridization and speciation in darwin’s finches: the role of sexual imprinting on a culturally transmitted trait,” Endless Forms: Species and Speciation, pp. 404–422, 1998.
[10] R. Bardeli, D. Wolff, F. Kurth, M. Koch, K. H. Tauchert, and K.-H. Frommolt, “Detecting bird sounds in a complex acoustic environment and application to bioacoustic monitoring,” Pattern Recognition Letters, vol. 31, no. 12, pp. 1524–1534, Sep. 2010.
[11] D. C. Cugler, C. B. Medeiros, and L. F. Toledo, “Managing animal sounds-some challenges and research directions,” in Proceedings V eScience Workshop-XXXI Brazilian Computer Society Conference, 2011.
[12] A. Farnsworth and R. W. Russell, “Monitoring flight calls of migrating birds from an oil platform in the northern gulf of mexico,” Journal of Field Ornithology, vol. 78, no. 3, pp. 279–289, 2007.
[13] D. Mennill and S. Vehrencamp, “Context-dependent functions of avian duets revealed through microphone array recordings and multi-speaker playback,” Current Biology, vol. 18, pp. 1314–1319, 2008.
[14] K. J. Odom and D. J. Mennill, “A quantitative description of the vocalizations and vocal activity of the barred owl,” The Condor, vol. 112, no. 3, pp. 549–560, 2010.
[15] S. Gaunt and A. McCallum, “Birdsong and Conservation,” in Nature’s music: the science of birdsong, 2004, ch. 12, pp. 343–362.
[16] A. Kirschel, M. Cody, Z. Harlow, V. Promponas, E. Vallejo, and C. Taylor, “Territorial dynamics of mexican ant-thrushes formicarius moniliger revealed by individual recognition of their songs,” Ibis, vol. 153, no. 2, pp. 255–268, 2011.
[17] D. T. Blumstein, D. J. Mennill, P. Clemins, L. Girod, K. Yao, G. Patricelli, J. L. Deppe, A. H. Krakauer, C. Clark, K. A. Cortopassi, S. F. Hanser, B. McCowan, A. M. Ali, and A. N. G. Kirschel, “Acoustic monitoring in terrestrial environments using microphone arrays: applications, technological considerations and prospectus,” Journal of Applied Ecology, vol. 48, no. 3, pp. 758–767, 2011.
[18] D. Chesmore, “The automated identification of taxa: Concepts and applications,” in Automated Taxon Identification in Systematics: Theory, Approaches and Applications, ser. Systematics Association special volumes, N. MacLeod, Ed. Boca Raton, FL: CRC Press, 2008, vol. 74, ch. 6, pp. 83–100.
[19] D. Chesmore, K.-H. Frommolt, D. Wolff, R. Bardeli, and S. Huebner, “Computational bioacoustics: New tools for assessing biological diversity,” May 2008, side Event at the ninth meeting of the Conference of the Parties (COP 9). Bonn, Germany.
[20] M. A. Acevedo and L. J. Villanueva-Rivera, “Using automated digital recording systems as effective tools for the monitoring of birds and amphibians,” Wildlife Society Bulletin, vol. 34, pp. 211 – 214, 2006.
[21] D. W. Armitage and H. K. Ober, “A comparison of supervised learning techniques in the classification of bat echolocation calls,” Ecological Informatics, vol. 5, no. 6, pp. 465 – 473, 2010.
[22] F. Briggs, X. Fern, and R. Raich, “Acoustic classification of bird species from syllables: an empirical study,” Oregon State University, Tech. Rep., 2009.
[23] E. P. Kasten, P. K. McKinley, and S. H. Gage, “Ensemble extraction for classification and detection of bird species,” Ecological Informatics, vol. 5, no. 3, pp. 153 –166, 2010.
[24] T. S. Brandes, “Automated sound recording and analysis techniques for bird surveys and conservation,” Bird Conservation International, vol. 18, no. S1, pp. S163–S173, 2008.
[25] R. Mason, P. Roe, M. Towsey, J. Zhang, J. Gibson, and S. Gage, “Towards an acoustic environmental observatory,” in eScience, 2008. eScience ’08. IEEE Fourth International Conference on, 2008, pp. 135–142.
[26] R. S. Rempel, C. M. Francis, J. N. Robinson, and M. Campbell, “Comparison of audio recording system performance for detecting and monitoring songbirds,” Journal of Field Ornithology, vol. 84, no. 1, pp. 86–97, 2013.
[27] D. Mennill, M. Battiston, D. R. Wilson, J. R. Foote, and S. M. Doucet, “Field test of an affordable, portable, wireless microphone array for spatial monitoring of animal ecology and behaviour,” Methods in Ecology and Evolution, vol. 3, pp. 704–712, 2012.
[28] K. A. Hobson, R. S. Rempel, H. Greenwood, B. Turnbull, and S. L. Van Wilgenburg, “Acoustic surveys of birds using electronic recordings: new potential from an omnidirectional microphone system,” Wildlife Society Bulletin, pp. 709–720, 2002.
[29] L. A. Venier, S. B. Holmes, G. W. Holborn, K. A. Mcilwrick, and G. Brown, “Evaluation of an automated recording device for monitoring forest birds,” Wildlife Society Bulletin, vol. 36, no. 1, pp. 30–39, 2012.
[30] T. S. Brandes, “Feature vector selection and use with hidden Markov models to identify frequency-modulated bioacoustic signals amidst noise,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 16, no. 6, pp. 1173 –1180, aug 2008.
[31] P. Caycedo-Rosales, “Estudio comparativo del canto entre poblaciones del soterrey de apolinar (cistothorus apolinari, troglodytidae) presentes en la cordillera oriental de los andes colombianos,” Universidad Nacional de Colombia, 2000, tesis de pregrado.
[32] B. Lakshminarayanan, R. Raich, and X. Fern, “A syllable-level probabilistic framework for bird species identification,” in Proceedings of the Fourth International Conference on Machine Learning and Applications. Los Alamitos, CA, USA: IEEE Computer Society, Dec. 2009, pp. 53–59.
[33] R. Bardeli, “Similarity search in animal sound databases,” IEEE Transactions on Multimedia, vol. 11, no. 1, pp. 68 –76, jan 2009.
[34] F. Briggs, R. Raich, and X. Z. Fern, “Audio classification of bird species: A statistical manifold approach,” in Proceedings of the Ninth IEEE International Conference on Data Mining. Los Alamitos, CA, USA: IEEE Computer Society, Dec. 2009, pp. 51–60.
[35] C.-J. Huang, Y.-J. Yang, D.-X. Yang, and Y.-J. Chen, “Frog classification using machine learning techniques,” Expert Systems with Applications, vol. 36, no. 2, pp. 3737–3743, 2009.
[36] D. Todt, “From birdsong to speech: a plea for comparative approaches,” Anais da Academia Brasileira de Ciências, vol. 76, no. 2, pp. 201–208, 2004.
[37] L. Ranjard and H. A. Ross, “Unsupervised bird song syllable classification using evolving neural networks,” Journal of the Acoustical Society of America, vol. 123, no. 6, pp. 4358 – 4368, 2008.
[38] M. Bramer, Principles of Data Mining. Springer, 2007.
[39] S. Fagerlund, “Bird species recognition using support vector machines,” EURASIP Journal on Advances in Signal Processing, vol. 2007, no. 1, pp. 64–64, 2007.
[40] I. Urazghildiiev, C. Clark, T. Krein, and S. Parks, “Detection and Recognition of North Atlantic Right Whale Contact Calls in the Presence of Ambient Noise,” IEEE Journal of Oceanic Engineering, vol. 34, no. 3, pp. 358–368, Jul. 2009.
[41] F. Briggs, B. Lakshminarayanan, L. Neal, X. Z. Fern, R. Raich, S. J. K. Hadley, A. S. Hadley, and M. G. Betts, “Acoustic classification of multiple simultaneous bird species: A multi-instance multi-label approach,” Journal of the Acoustical Society of America, vol. 131, no. 6, pp. 4640–4650, Oct. 2012.
[42] C.-H. Chou, P.-H. Liu, and B. Cai, “On the studies of syllable segmentation and improving MFCCs for automatic birdsong recognition,” in Proceedings of the 2008 IEEE Asia-Pacific Services Computing Conference. Washington, DC, USA: IEEE Computer Society, 2008, pp. 745–750.
[43] A. Härmä, “Automatic identification of bird species based on sinusoidal modeling of syllables,” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP ’03, vol. 5, Apr. 2003, pp. 545–548.
[44] D. Stowell and M. D. Plumbley, “Birdsong and C4DM: A survey of UK birdsong and machine recognition for music researchers,” Centre for Digital Music, Queen Mary, University of London, London, UK, Tech. Rep. C4DM-TR-09-12, Jul. 2011.
[45] L. Neal, F. Briggs, R. Raich, and X. Z. Fern, “Time-frequency segmentation of bird song in noisy acoustic environments,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011, May 2011, pp. 2012 –2015.
[46] C.-H. Lee, S.-B. Hsu, J.-L. Shih, and C.-H. Chou, “Continuous Birdsong Recognition Using Gaussian Mixture Modeling of Image Shape Features,” IEEE Transactions on Multimedia, vol. 15, pp. 454–464, 2013.
[47] D. Mitrovic, M. Zeppelzauer, and C. Breiteneder, “Features for content-based audio retrieval,” in Advances in Computers: Improving the Web, ser. Advances in Computers, M. V. Zelkowitz, Ed. Elsevier, 2010, vol. 78, pp. 71 – 150.
[48] S. Ntalampiras, I. Potamitis, and N. Fakotakis, “Probabilistic Novelty Detection for Acoustic Surveillance Under Real-World Conditions,” IEEE Transactions on Multimedia, vol. 13, no. 4, pp. 713–719, Aug. 2011.
[49] A. Selin, J. Turunen, and J. T. Tanttu, “Wavelets in recognition of bird sounds,” EURASIP J. Appl. Signal Process., vol. 2007, no. 1, pp. 141–141, Jan. 2007.
[50] J. L. Goyette, R. W. Howe, A. T. Wolf, and W. D. Robinson, “Detecting tropical nocturnal birds using automated audio recordings,” Journal of Field Ornithology, vol. 82, no. 3, pp. 279–287, 2011.
[51] C. M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics), 1st ed. Springer, 2007.
[52] R. Duda, P. Hart, and D. Stork, Pattern Classification. Wiley, 2001.
[53] L. R. Rabiner, “A tutorial on hidden Markov models and selected applications in speech recognition,” Proceedings of the IEEE, vol. 77, no. 2, pp. 257 –286, May 1989.
[54] D. Chesmore, “Automated bioacoustic identification of species,” Anais da Academia Brasileira de Ciências, vol. 76, no. 2, pp. 436–440, 2004.
[55] J. Wimmer, M. Towsey, B. Planitz, P. Roe, and I. Williamson, “Scaling acoustic data analysis through collaboration and automation,” in e-Science (e-Science), 2010 IEEE Sixth International Conference on. IEEE, 2010, pp. 308–315.
[56] W. J. Sutherland, From individual behaviour to population ecology. Oxford University Press Inc., 1996.
[57] P. K. McGregor, T. M. Peake, and G. Gilbert, “Communication behaviour and conservation,” in Behaviour and Conservation. Cambridge University Press, 2000.
[58] H. Slabbekoorn, “Singing in the wild: the ecology of birdsong,” in Nature’s music: the science of birdsong, 2004, ch. 1, pp. 178–2005.
[59] V. Deecke, J. Ford, and P. Spong, “Quantifying complex patterns of bioacoustic variation: use of a neural network to compare killer whale (orcinus orca) dialects,” The Journal of the Acoustical Society of America, vol. 105, p. 2499, 1999.
[60] H. Mills, “Automatic detection and classification of nocturnal migrant bird calls,” The Journal of the Acoustical Society of America, vol. 97, p. 3370, 1995.
[61] A. Taylor, “Bird flight call discrimination using machine learning,” The Journal of the Acoustical Society of America, vol. 97, p. 3370, 1995.
[62] W. M. Tyler, “The call-notes of some nocturnal migrating birds,” The Auk, pp. 132–141, 1916.
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Published
Sep 26, 2013
Article Details
How to Cite
Caycedo-Rosales, P. C., Ruiz-Muñoz, J. F., & Orozco-Alzate, M. (2013). Automated Recognition of Bioacoustic Signals: a Review of Methods and Applications. Ingeniería Y Ciencia, 9(18), 171–195. https://doi.org/10.17230/ingciecia.9.18.10
Issue
Section
Reviews
Supporting Agencies
Instituto de Investigación de Recursos Biológicos “Alexander von Humboldt” and Universidad Nacional de Colombia, Sede Manizales
Author Biographies
Paula Catalina Caycedo-Rosales, Instituto de Investigación de Recursos Biológicos Alexander von Humboldt
Laboratorio de Biogeografía Aplicada y Bioacústica, investigadoraJosé Francisco Ruiz-Muñoz, Universidad Nacional de Colombia, sede Manizales
Grupo de Procesamiento y Reconocimiento de Señales, Estudiante de Doctorado en Ingeniería-Línea Automática
Mauricio Orozco-Alzate, Universidad Nacional de Colombia, sede Manizales
Departamento de Informática y Computación, Profesor Asociado
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